Rotational Mutation Genetic Algorithm on optimization Problems
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Optimization problem, nowadays, have more application in all major but they have problem in computation. Calculation of the optimum point in the spaces with the above dimensions is very time consuming. In this paper, there is presented a new approach for the optimization of continuous functions with rotational mutation that is called RM. The proposed algorithm starts from the point which has best fitness value by elitism mechanism. Then, method of rotational mutation is used to reach optimal point. In this paper, RM algorithm is implemented by GA(Briefly RMGA) and is compared with other well- known algorithms: DE, PGA, Grefensstette and Eshelman [15, 16] and numerical and simulation results show that RMGA achieve global optimal point with more decision by smaller generations.
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